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Jurnal Computer Science and Information Technology (CoSciTech)
Published by Universitas Muhammadiyah Riau
ISSN : 2723567X     EISSN : 27235661     DOI : https://doi.org/10.37859/coscitech
Core Subject : Science,
Computer Science & IT
Jurnal CoSciTech (Computer Science and Information Technology) merupakan jurnal peer-review yang diterbitkan oleh Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Univeritas Muhammadiyah Riau (UMRI) sejak April tahun 2020. Jurnal CoSciTech terdaftar pada PDII LIPI dengan Nomor ISSN 2723-5661 (Online) dan 2723-567X (Cetak). Jurnal CoSciTech berkomitmen menjadi jurnal nasional terbaik untuk publikasi hasil penelitian yang berkualitas dan menjadi rujukan bagi para peneliti. Jurnal CoSciTech menerbitkan paper secara berkala dua kali setahun yaitu pada bulan April dan Oktober. Semua publikasi di jurnal CoSciTech bersifat terbuka yang memungkinkan artikel tersedia secara bebas online tanpa berlangganan.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 388 Documents
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Klasifikasi kendaraan bermotor berdasarkan jumlah gandar menggunakan adaptive minimal ensemble Al Hakim, Abdurrahman; Muttaqin, Faisal; Hendra Maulana
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11239

Abstract

The increasing volume of motor vehicles requires automated monitoring for the classification of heavy vehicle categories (Category I–V) based on the number of axles using side-view cameras. This process represents a complex fine-grained visual classification challenge due to the similar body shapes of trucks. To address the dilemma between the need for high accuracy and computational efficiency, this study implements an Adaptive Minimal Ensemble (AME) architecture that adaptively combines small-scale models.  The model is evaluated using a confusion matrix along with accuracy, precision, recall, and F1-score metrics. The testing results demonstrate that a single EfficientNetV2-S model is only able to achieve a maximum accuracy of 83% and exhibits significant limitations in extracting crucial distinguishing features, leading to misclassification of Category 4 and 5 vehicles. In contrast, the AME architecture, which utilizes the two best-performing EfficientNetV2-S base models, successfully achieves a substantial performance improvement with 95% accuracy, 95.21% precision, 95% recall, and a 94.99% F1-score.  In conclusion, the adaptive layer mechanism in AME is proven to be highly effective in compensating for the individual prediction weaknesses of its base models, resulting in a significantly more precise vehicle classification monitoring system.
Implementasi Algoritma Random Forest pada Web-App Sebagai Instrumen Deteksi Dini Penyakit Diabetes Fauzan, Habibul; Haerani, Elin; Kurnia, Fitra; Yanti, Novi
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11261

Abstract

Diabetes is a chronic metabolic disease and one of the leading causes of death worldwide, with the number of sufferers projected to reach 1.3 billion by 2050. Delayed diagnosis remains a primary challenge, as nearly half of those affected are unaware of their condition in the early stages, thereby increasing the risk of fatal complications. Data mining approaches using classification algorithms have been widely utilized for early screening. However, the development of medical record models is often hindered by imbalanced data, which causes models to be biased toward the majority class and reduces detection sensitivity for the minority class (patients with diabetes). Furthermore, there is a lack of research integrating these predictive models into responsive application interfaces for end-users. Consequently, this study implements Random Forest optimized with the SMOTE (Synthetic Minority Over-sampling Technique) into a web-based application to serve as a practical early detection tool. Random Forest was selected for its ability to handle complex data and reduce the risk of overfitting. The research stages include data preprocessing, balancing training data using SMOTE, model parameter adjustment through hyperparameter tuning with Grid Search, and the development of a client-server architecture using AstroJS and Flask. The evaluation results demonstrate that the use of SMOTE significantly improves the model's ability to identify the minority class. The model achieved a Recall of 75.0% and an overall accuracy of 95.8%, effectively minimizing False Negative errors. The developed application was verified through Black Box Testing and was declared successful as a responsive and accessible early detection tool for both healthcare professionals and the general public.
Implementasi LoRa pada Monitoring Tempat Pembakaran Sampah berbasis Website: Implementation of LoRa in a Website – Based Waste Incinerator site Monitoring System Muhammad Raehan Maulana; Hafiz Muhardi; Midyanti, Dwi Marisa
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11276

Abstract

Waste accumulation is a common problem caused by low public awareness of waste management and suboptimal waste handling by sanitation workers. Waste incineration can serve as a solution, but it has the potential to cause undesirable consequences if not properly supervised; therefore, a monitoring system capable of tracking the waste incineration process in real-time is necessary. With the rapid advancement of technology, wireless technology can be utilized to address this issue. However, most wireless technologies still rely on internet networks, making them less effective in areas with limited internet connectivity. Therefore, this study proposes the use of LoRa technology as a solution for data transmission without reliance on an internet network. The developed system can monitor waste incineration sites using MQ-2 sensors and flame sensors in real-time via a website, utilizing the LoRa SX1278 data transmission system. Implementation results show that the monitoring system can detect residual smoke and fire from waste incineration sites and monitor them up to a distance of 150 meters. Signal quality in LoRa SX1278 transmission is expressed in RSSI (Received Signal Strength Indicator) units, with an “excellent” signal category achieved up to a distance of 40 meters in the tests conducted.
Convolutional Neural Network dengan Arsitektur InceptionV3 untuk Klasifikasi Citra Makanan Berdasarkan Asal Daerah Jawa dan Sumatera Khasanah, Diva Nayla; Firdaus, Rahmad
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11331

Abstract

This study aims to improve the accuracy of classifying traditional food images based on the regions of Java and Sumatra using the Convolutional Neural Network (CNN) algorithm with the InceptionV3 architecture. Traditional foods from these two regions are often difficult to distinguish due to visual similarities. The dataset consists of 472 food images processed through segmentation, augmentation, and rescaling. The InceptionV3 model was selected for its ability to capture complex visual patterns with high efficiency. The training process employed the Adam optimizer, a learning rate of 0.001, and a 50% dropout regularization technique to prevent overfitting. The model was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the model achieved an accuracy of 90.42%.precision of 91.07%, recall of 92.72%, and F1-score of 90%, significantly improving compared to previous research, which only achieved an accuracy of 64% using CNN without a specific architecture. This study is expected to contribute to the preservation of local culinary culture and support the promotion of tourism and technology-based culinary industries in Indonesia.
KLASIFIKASI PENYAKIT DAUN SINGKONG MENGGUNAKAN ARSITEKTUR EFFICIENTNET BERBASIS TRANSFER LEARNING Ramadani, Syahrul; Ridha, Muh. Rasyid; Samsudin, Samsudin
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11335

Abstract

Cassava (Manihot esculenta) is a strategic agricultural commodity whose productivity is frequently threatened by leaf diseases such as bacterial blight, brown streak, green mottle, and mosaic disease. Manual identification by humans tends to be subjective, time-consuming, and prone to error. This study aims to develop an automatic and intelligent Cassava leaf disease classification system based on Deep Learning that is both accurate and efficient. To overcome the computational burden of conventional models and address real-world data challenges, such as class imbalance and lighting variations, this research proposes the use of the EfficientNet architecture combined with the Transfer Learning method. The model utilizes pre-trained weights from ImageNet to accelerate convergence and optimize visual feature extraction. Experimental results on the Cassava leaf image dataset show that the proposed model successfully achieved an accuracy rate of 81%. These findings demonstrate that the EfficientNet approach provides objective predictions with high computational efficiency. This research has significant potential for implementation in portable devices as an early detection tool for farmers, supporting rapid mitigation actions and maintaining global food security stability
Sistem deteksi kebocoran gas lpg berbasis internet of things: Implementation of the internet of things for LPG gas leak detection system Aisa, Sitti; Husain; Alif Al Qadri
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.10305

Abstract

LPG gas has become a primary household necessity due to its cost-effectiveness and ease of use. However, the risk of gas leaks, which can lead to fires, remains a significant drawback, primarily due to a lack of public awareness of how to mitigate them. This research aims to design and build a prototype of an integrated Internet of Things (IoT)-based security system for early detection and mitigation of gas leaks. The system uses two MQ-5 gas sensors to monitor gas concentrations in two different areas and two infrared (IR) sensors to control automatic door access. Data from the sensors is acquired by an ESP8266 microcontroller, which then sends it wirelessly to a server for processing. The system provides a multi-layered response: early warning via buzzer, real-time data visualization on a web dashboard, and instant notifications to users via Telegram. Test results show the system has a reasonable response, where the MQ-5 sensor is able to detect gas and process it on the server in approximately 20 seconds, and the infrared sensor accurately detects objects with 95% accuracy. With the implementation of IoT technology, the system is not only able to continuously monitor gas concentrations in the air, but also provide automatic response actions, thereby increasing alertness and significantly reducing the risk of fires due to gas leaks.
the Sistem Pakar Analisis Love Language untuk Menilai Kualitas Komunikasi Pasangan Milenial Menggunakan Forward Chaining dan Certainty Factor: Indonesia Caniago, Deosa Putra; Tan, Vincent; Pakpahan, Sindy Silvia Soarta; Susyantika, Salsabila Adine; Sulthony, M Rachmat; Aswirawan, Maria Yosefina Meinadia Sekar Kinanti
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.10598

Abstract

This research aims to analyze the influence of love language on communication quality among millennial couples in Indonesia. Millennials, who are highly immersed in digital interaction, tend to communicate quickly and text-based, making romantic interactions dynamic yet prone to misunderstanding. A quantitative survey was conducted involving 180 respondents aged 22–35 who had been in a relationship for at least one year. A Likert-scale questionnaire measured preferences across the five love language categories—Words of Affirmation, Quality Time, Acts of Service, Physical Touch, and Receiving Gifts, as well as communication quality indicators including openness, clarity, trust, and emotional intimacy. Findings indicate that all five love languages significantly affect communication quality, each with varying levels of influence. Quality Time emerged as the most dominant category with an estimated tendency of 92–96%, followed by Words of Affirmation with a dominance level above 85%. These results suggest that understanding and adapting to one’s partner’s love language can serve as an effective communication strategy to increase emotional closeness, reduce conflict, and strengthen relationship harmony among millennial couples.
Pengembangan Web Scraper Menggunakan Algoritma DFS Untuk Analisis Penyusupan Situs Judi Online Pada Domain AC.ID, GO.ID, MY.ID, .ID, Dan SCH.ID Ramadhan, Angga Rizki; Dasuki, Moh.; Muharom, Lutfi Ali
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.10910

Abstract

The infiltration of online gambling into official domains such as ac.id, go.id, my.id, .id, and sch.id has become a serious issue because it can damage institutional reputation, disrupt information security, and reduce public trust. One of the common objectives of this infiltration practice is to boost the SEO ranking of the gambling sites by exploiting the authority of official domains. This research aims to develop a web scraper using the Depth First Search DFS algorithm capable of performing deep traversal on website structures and automatically analyzing the infiltration of online gambling links. The system conducts crawling, extraction of html elements containing gambling-related links, text analysis, and keyword pattern matching. Testing was carried out in several stages, starting with an initial evaluation on 40 domains followed by a formal accuracy test on 50 domains. The results show that the system performs stably and consistently when conducting traversal using DFS. The achieved accuracy reached 98%, with 49 correct analyses and 1 detection error. Further evaluation on an additional 60 domains increased the accuracy to 98,33% after improvements were applied to the analysis module.
Analisis Usability Testing Aplikasi Transportasi Digital Menggunakan System Usability Scale (SUS) Aghniya, Insan Rezqy; Dari, Wulani
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.10955

Abstract

The development of information technology in the transportation sector has transformed the way people carry out their mobility activities, particularly through the use of digital transportation applications. Although these applications offer various conveniences, several issues are still encountered in their practical use, which may affect user comfort and satisfaction. Therefore, usability becomes a crucial aspect in evaluating the quality and performance of digital transportation applications. This study applies the System Usability Scale (SUS) method using a Likert scale in the research questionnaire to analyze usability. Primary data were collected through observation, interviews, literature review, and the distribution of SUS questionnaires to users of digital transportation applications in the DKI Jakarta area aged between 17 and 45 years. The results of the analysis show an average score of 73.4, which falls into the acceptable category and indicates that the application is well accepted by users. This research contributes an evaluation of the usability level of digital transportation applications, which can serve as a basis for system improvement recommendations, particularly in terms of usage flow, user interface, and user feedback, as well as a reference for future studies related to user experience development
Implementasi Fuzzy Mamdani untuk Rekomendasi Total Kalori Harian Bagi Penderita Diabetes Tipe 2 : Implementasi Fuzzy Mamdani untuk Rekomendasi Total Kalori Harian Bagi Penderita Diabetes Tipe 2 Widiasari, Putu Puja Diva; Siregar, Alda Cendekia; Octariadi, Barry Ceasar
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11027

Abstract

Type 2 Diabetes Mellitus is a metabolic disease that requires precise regulation of daily calorie intake to maintain stable blood sugar levels. Determining calorie requirements is not simple because it must take into account factors such as age, gender, body mass index (BMI), and physical activity level. This study aims to develop a Mamdani fuzzy logic-based expert system to provide recommendations for daily calorie requirements for people with type 2 diabetes. The system process is carried out through the stages of fuzzification, inference, aggregation, and defuzzification using the centroid method. Testing was conducted using 20 type 2 diabetes patient data with input variables of age, height, weight, gender, and physical activity. The testing methods used were accuracy and black-box. Accuracy testing was performed by comparing the system's results with manual calculations based on medical standards, while black-box testing ensured that the system functioned as designed. The results showed that the system had an accuracy rate of 80%, making it sufficiently valid and usable as a tool for recommending daily calorie intake to support diet management for type 2 diabetes patients.

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